Note: Cells in this notebook are meant to be executed in order* (from top to bottom). Some of the later examples depend on previous ones!*
Note: Cells in this notebook are meant to be executed in order* (from top to bottom). Some of the later examples depend on previous ones!*
First we import all libraries necessary to run code in this notebook. Some of them are python standard libraries, that are installed by default. Other libraries can be installed using the conda package manager via:
!conda install -y -c conda-forge <PACKAGE>
A full list of dependencies required to run this notebook is available in the file environment.yml at https://github.com/TUW-GEO/c3s_sm-tutorials. If you are on Binder (click badge at the top), all necessary dependencies are already installed.
We also make sure to install the CDS API via pip by running:
%%capture --no-display
!pip install cdsapi
import os
import cdsapi
from pathlib import Path
from tempfile import TemporaryDirectory
import ipywidgets as widgets
import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
import xarray as xr
from scipy.stats import theilslopes
import numpy as np
import shutil
import pandas as pd
import zipfile
from collections import OrderedDict
%matplotlib inline
The file utils.py is part of this package. It contains helper functions that are not relevant to understand contents of the notebook and therefore transferred to a separate file.
import utils as utils
Satellites sensors can observe the amount of water stored in the top layer of the soil from space. Various satellite missions from different space agencies provide measurements of radiation from the Earth's surface across different (microwave) frequency domains (Ku-, X,- C- and L-band), which are related to the amount of water stored in the soil. There are two types of sensors that are used to measure this information: passive systems (radiometer) and active systems (radar).
For a detailed description, please see the C3S Soil Moisture Algorithm Theoretical Baseline Document, which is available together with the data at the Copernicus Climate Data Store.
Brightness temperature is the observable of passive sensors (in $°K$). It is a function of kinetic temperature and emissivity. Wet soils have a higher emissivity than dry soils and therefore a higher brightness temperature. Passive soil moisture retrieval uses this difference between kinetic temperature and brightness temperature to model the amount of water available in the soil of the observed area, while taking into account factors such as the water held by vegetation.
NASA's SMAP and ESA's SMOS satellites are examples for L-band radiometer missions. They are suitable for retrieving soil moisture globally, even when vegetation is present in a scene.
Different models to retrieve Soil Moisture from brightness temperature measurements exist. One of the them is the Land Parameter Retrieval Model (Owe et al., 2008, Owe et al., 2001, and van der Schalie et al., 2016). This model is used to derive soil moisture for all passive sensors in C3S.
The PASSIVE product of C3S Soil Moisture contains merged observations from passive systems only. It is given in volumetric units $[m^3 / m^3]$.
Active systems emit radiation in the microwave domain (C-band in C3S). As the energy pulses emitted by the radar hit the Earth's surface, a scattering effect occurs and part of the energy is reflected back, containing information on the surface state of the observed scene. The received energy is called “backscatter”, with rough and wet surfaces producing stronger signals than smooth or dry surfaces. Backscatter comprises reflections from the soil surface layer (“surface scatter”), vegetation (“volume scatter”) and interactions of the two.
ESA's ERS-1 and ERS-2, as well as EUMETSAT's Metop ASCAT sensors are active systems used in C3S soil moisture. In the case of Metop ASCAT, C3S Soil Moisture uses the Surface Soil Moisture products directly provided by H SAF, based on the WARP algorithm (Wagner et al., 1999, Wagner et al., 2013).
The ACTIVE product of C3S Soil Moisture contains merged observations from active systems only. It is given in relative units $[\%$ $saturation]$.
Single-sensor products are limited by the life time of the satellite sensors. Climate change assessments, however, require the use of long-term data records, that span over multiple decades and provide consistent and comparable observations. The C3S Soil Moisture record therefore merges the observations from more than 15 sensors into one harmonized record. The main 2 steps of the product generation include scaling all sensors to a common reference, and subsequently merging them by applying a weighted average, where sensor with a lower error are assigned a higher weight. The following figure shows all satellite sensors merged in the PASSIVE (only radiometers), ACTIVE (only scatterometers) and COMBINED (scatterometers and radiometers) product (data set version v202212).
C3S Soil Moisture is based on the ESA CCI SM algorithm, which is described in Dorigo et al., 2017 and Gruber et al., 2019.
The COMBINED product is also given in volumetric units $[m^3 / m^3]$. However, the absolute values depend on the scaling reference, which is used to bring all sensors into the same dynamic range. In this case we use Soil Moisture simulations for the first 10 cm from the GLDAS Noah model (Rodell et al., 2004).
Different products and versions for C3S Soil Moisture are available on the Copernicus Climate Data Store. In general, there are 2 types of data records:
There are different options to specify a valid C3S Soil Moisture data request. You can use the CDS GUI to generate a valid request (button Show API Request) and copy/paste it to your python script as shown below. To summarize the options:
variable: Either volumetric_surface_soil_moisture (must be chosen to download the PASSIVE or COMBINED data) or surface_soil_moisture (required for ACTIVE product)type_of_sensor: One of active, passive or combined_passive_and_active (must match with the selected variable!)time_aggregation: month_average, 10_day_average, or day_average. The original data is daily. Monthly and 10-daily averages are often required for climate analyses and therefore provided for convenience.year: a list of years to download data for (COMBINED and PASSIVE data is available from 1978 onward, ACTIVE starts in 1991)month: a list of months to download data.day: a list of days to download data for (note that for the monthly data, day must always be '01'. For the 10-daily average, valid days are: '01', '11', '21' (therefore the day always refers to the start of the period the data represents).area: (optional) Coordinates of a bounding box to download data for.type_of_record: cdr and/or icdr. It is recommended to select both, to use whichever data is available (there is no overlap between ICDR and CDR of a major version).version: Data record version, currently available: v201706.0.0, v201812.0.0, v201812.0.1, v201912.0.0, v202012.0.0, v202012.0.1, v202012.0.2 (new versions are added regularly). Sub-versions indicate new data that is meant to replace the previous sub-versions (e.g. due to processing errors). It is therefore recommended to pass all sub-versions and use the file with the highest version for any time stamp in case of duplicate time stamps.format: Either zip or tgz. Archive format that holds the individual netcdf images.In order to download data from the Climate Data Store (CDS) via the API you need:
If you do not provide a valid KEY in the next cell, the following API request will fail. However, you can then still continue with the example data provided together with this notebook, which is the same data you would get if the query is not changed: i.e., monthly volumetric surface soil moisture from passive observations at version v202012 over Europe, from CDR & ICDR. The provided example data is stored in the repository as this notebook (./DATA/sm_monthly_passive_v202012.zip). It is recommended to use monthly data, as some of the examples in this notebook will not work with daily or 10-daily images!
URL = "https://cds.climate.copernicus.eu/api/v2"
# If you have a valid key, set it in the following line:
KEY = "######################################"
try:
c = cdsapi.Client(url=URL, key=KEY)
DATA_PATH = Path("DATA") / "my_data.zip"
c.retrieve(
"satellite-soil-moisture",
{
"variable": "volumetric_surface_soil_moisture",
"type_of_sensor": "passive",
"time_aggregation": "month_average", # required for examples in this notebook
"year": [str(y) for y in range(1991, 2023)],
"month": [f"{m:02}" for m in range(1, 13)],
"day": "01",
"area": [72, -11, 34, 40],
"type_of_record": ["cdr", "icdr"],
"version": ["v202012.0.0", "v202012.0.1", "v202012.0.2"],
"format": "zip",
},
DATA_PATH,
)
except Exception as e:
DATA_PATH = Path("DATA") / "sm_monthly_passive_v202012.zip"
print(
"Could not download data from CDS using the passed request and/or API Key.\n"
f"The following error was raised: \n {e} \n \n"
f"We therefore continue with the data provided in: {DATA_PATH}"
)
2023-02-02 20:45:38,812 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/satellite-soil-moisture
Could not download data from CDS using the passed request and/or API Key. The following error was raised: 'tuple' object is not callable We therefore continue with the data provided in: DATA/sm_monthly_passive_v202012.zip
From the previous cell, we have a variable DATA_PATH which points to a .zip archive (either newly downloaded or provided) containing the selected data from CDS as individual images. We use the library xarray to read these data, but first we have to extract them. In the next cell we extract all files from the downloaded .zip archive into a new folder. We do this using standard python libraries:
# Setting up a temporary folder to extract data to:
extracted_data = Path(f"{DATA_PATH}_extracted")
if os.path.exists(extracted_data):
shutil.rmtree(extracted_data)
os.makedirs(extracted_data)
# Extract all files from zip:
with zipfile.ZipFile(DATA_PATH, "r") as archive:
archive.extractall(extracted_data)
We can then use the function xarray.open_mfdataset to load all extracted files and concatenate them along the time dimension automatically. This way we get a 3-dimensional (longitude, latitude, time) data cube, that we store in a global variable DS. In addition we extract the unit and valid range of the soil moisture variable from the netCDF metadata (SM_UNIT and SM_RANGE). Finally we plot a table that shows the contents of DS.
DS = xr.open_mfdataset(os.path.join(extracted_data, "*.nc"))
SM_UNIT = DS["sm"].attrs["units"]
SM_RANGE = DS["sm"].attrs["valid_range"]
display(DS)
<xarray.Dataset>
Dimensions: (lat: 152, lon: 204, time: 384)
Coordinates:
* lat (lat) float32 71.88 71.62 71.38 71.12 ... 34.62 34.38 34.12
* lon (lon) float32 -10.88 -10.62 -10.38 -10.12 ... 39.38 39.62 39.88
* time (time) datetime64[ns] 1991-01-01 1991-02-01 ... 2022-12-01
Data variables:
sm (time, lat, lon) float32 dask.array<chunksize=(1, 152, 204), meta=np.ndarray>
sensor (time, lat, lon) float32 dask.array<chunksize=(1, 152, 204), meta=np.ndarray>
freqbandID (time, lat, lon) float32 dask.array<chunksize=(1, 152, 204), meta=np.ndarray>
nobs (time, lat, lon) float32 dask.array<chunksize=(1, 152, 204), meta=np.ndarray>
Attributes: (12/40)
title: C3S Surface Soil Moisture merged PASSIVE Product
institution: EODC (AUT); TU Wien (AUT); VanderSat B.V. (NL)
contact: C3S_SM_Science@eodc.eu
source: LPRMv6/SMMR/Nimbus 7 L3 Surface Soil Moisture...
platform: Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1...
sensor: SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMO...
... ...
id: C3S-SOILMOISTURE-L3S-SSMV-PASSIVE-MONTHLY-199...
history: 2021-03-29T13:46:57.630282 mean calculated
date_created: 2021-03-29T13:46:57Z
time_coverage_start: 1990-12-31T12:00:00Z
time_coverage_end: 1991-01-31T12:00:00Z
time_coverage_duration: P1MNow that we have a data cube to work with, we can start by visualizing some of the soil moisture data. In this first example we create an interactive map, to show the absolute soil moisture values for a certain date. In addition we will use this example to define some locations and study areas we can use in the rest of the notebook and display their location on the map.
First we define some potential study areas that we can use in the following examples. Below you can find a list of bounding boxes, plus one 'focus point' in each bounding box. You can add your own study area to the end of the list. Make sure to pass the coordinates in the correct order: Each line consists of:
(<STUDY_AREA_NAME>, ([<BBOX min. Lon.>, <BBOX max. Lon.>, <BBOX min. Lat.>, <BBOX max. Lat.>], [<POINT Lon.>, <POINT Lat.>]))
BBOXES = OrderedDict(
[
# (Name, ([min Lon., max Lon., min Lat., max Lat.], [Lon, Lat])),
("Balkans", ([16, 29, 36, 45], [24, 42])),
("Cental Europe", ([6, 22.5, 46, 51], [15, 49.5])),
("France", ([-4.8, 8.4, 42.3, 51], [4, 47])),
("Germany", ([6, 15, 47, 55], [9, 50])),
("Iberian Peninsula", ([-10, 3.4, 36, 44.4], [-5.4, 41.3])),
("Italy", ([7, 19.0, 36.7, 47.0], [14, 42])),
("S-UK & N-France", ([-5.65, 2.5, 48, 54], [-1, 52])),
]
)
DS is a xarray.Dataset, which comes with a lot of functionalities. For example we can create a simple map visualization of soil moisture and the number of observations for a certain date. Using ipython widgets, we can add a slider that changes the date to plot.
When browsing through the images of different dates, it can be seen that the number of observations is much larger in later periods of the record than in earlier ones due to the larger number of available satellites.
We also include a selection for one of the previously defined study areas. Note that the study area that is finally chosen in this example is stored in the global variable STUDY_AREA, which is again used later on in the notebook!
STUDY_AREA = None
# Widgets for this example:
# 1) Slider to select date to plot, 2) Dropdown field for study area
dates = [str(pd.to_datetime(t).date()) for t in DS["time"].values]
slider = widgets.SelectionSlider(
options=dates,
value=dates[-1],
description="Select a date to plot:",
continuous_update=False,
style={"description_width": "initial"},
layout=widgets.Layout(width="40%"),
)
area = widgets.Dropdown(
options=list(BBOXES.keys()), value="Germany", description="Study Area:"
)
@widgets.interact(date=slider, area=area)
def plot_soil_moisture(date: str, area: str):
"""
Plot the `soil moisture` and `nobs` variable of the previously loaded Dataset.
"""
fig, axs = plt.subplots(
1, 2, figsize=(17, 5), subplot_kw={"projection": ccrs.PlateCarree()}
)
# Extract and plot soil moisture image for chosen date:
p_sm = (
DS["sm"]
.sel(time=date)
.plot(
transform=ccrs.PlateCarree(),
ax=axs[0],
cmap=utils.CM_SM,
cbar_kwargs={"label": f"Soil Moisture [{SM_UNIT}]"},
)
)
axs[0].set_title(f"{date} - Soil Moisture")
# Extract and plot nobs image for chosen date
if "nobs" in DS.variables:
# nobs is only available for monthly and 10-daily data
p_obs = (
DS["nobs"]
.sel(time=date)
.plot(
transform=ccrs.PlateCarree(),
ax=axs[1],
vmax=31,
vmin=0,
cmap=plt.get_cmap("YlGnBu"),
cbar_kwargs={"label": "Days with valid observations"},
)
)
axs[1].set_title(f"{date} - Data coverage")
else:
p_obs = None
bbox = BBOXES[area][0]
point = BBOXES[area][1]
# Add basemape features
for p in [p_sm, p_obs]:
if p is None:
continue
p.axes.add_feature(cartopy.feature.LAND, zorder=0, facecolor="gray")
p.axes.coastlines()
# Add study areas to first map
axs[0].plot(
[point[0]],
[point[1]],
color="red",
marker="X",
markersize=10,
transform=ccrs.PlateCarree(),
)
axs[0].plot(
[bbox[0], bbox[0], bbox[1], bbox[1], bbox[0]],
[bbox[2], bbox[3], bbox[3], bbox[2], bbox[2]],
color="red",
linewidth=3,
transform=ccrs.PlateCarree(),
)
for ax in axs:
if ax is not None:
gl = ax.gridlines(
crs=ccrs.PlateCarree(), draw_labels=True, alpha=0.25
)
gl.right_labe, gl.top_label = False, False
# Set global variable (to access in later examples)
global STUDY_AREA
STUDY_AREA = {"name": area, "bbox": bbox, "point": point}
In the following two examples we use the study area selected in Example 1. We use the chosen 'focus point' assigned to the study area (marked by the red X in the first map of the previous example) to extract a time series from the loaded stack at this location. We then compute the climatological mean (and standard deviation) for the chosen time series using the selected reference period. Finally, we subtract the climatology from the absolute soil moisture to derive a time series of anomalies. Anomalies therefore indicate the deviation of a single observation from the average (normal) conditions. A positive anomaly can be interpreted as "wetter than usual" soil moisture conditions, while a negative anomaly indicates "drier than usual" states.
There are different ways to express anomalies:
# Widgets for this example:
# 1) Slider to select baseline period, 2) Dropdown field to select anomaly metric
baseline_sider = widgets.IntRangeSlider(
min=1991,
max=2021,
value=[1991, 2020],
step=1,
style={"description_width": "initial"},
continuous_update=False,
description="Climatology reference / baseline period [year from, year to]:",
layout=widgets.Layout(width="50%"),
)
metric_dropdown = widgets.Dropdown(
options=["Absolute Anomalies", "Relative Anomalies", "Z-Scores"],
value="Absolute Anomalies",
description="Metric:",
)
@widgets.interact(baseline=baseline_sider, metric=metric_dropdown)
def plot_ts_components(baseline: tuple, metric: str):
"""
Compute and visualise climatology and anomalies for the loaded soil moisture
time series at the study area focus point.
"""
# Extract data at location:
lon, lat = float(STUDY_AREA["point"][0]), float(STUDY_AREA["point"][1])
ts = DS["sm"].sel(lon=lon, lat=lat, method="nearest").to_pandas()
# Compute scores:
clim_data = ts.loc[f"{baseline[0]}-01-01":f"{baseline[1]}-12-31"]
clim_std = pd.Series(
clim_data.groupby(clim_data.index.month).std(), name="climatology_std"
)
clim_mean = pd.Series(
clim_data.groupby(clim_data.index.month).mean(), name="climatology"
)
ts = pd.DataFrame(ts, columns=["sm"]).join(
on=ts.index.month, other=clim_mean
)
ts["climatology_std"] = ts.join(on=ts.index.month, other=clim_std)[
"climatology_std"
]
ts["abs_anomaly"] = ts["sm"] - ts["climatology"]
ts["rel_anomaly"] = (
(ts["sm"] - ts["climatology"]) / ts["climatology"] * 100
)
ts["z_score"] = (ts["sm"] - ts["climatology"]) / ts["climatology_std"]
# Generate plots:
fig, axs = plt.subplots(3, 1, figsize=(10, 7))
ts["sm"].plot(
ax=axs[0],
title=f"Soil Moisture at focus point of `{STUDY_AREA['name']}` study area "
f"(Lon: {lon}° W, Lat: {lat}° N)",
ylabel=f"SM $[{SM_UNIT}]$",
xlabel="Time [year]",
)
for i, g in clim_data.groupby(clim_data.index.year):
axs[1].plot(range(1, 13), g.values, alpha=0.2)
clim_mean.plot(
ax=axs[1],
color="blue",
title=f"Soil Moisture Climatology at Lon: {lon}° W, Lat: {lat}° N",
ylabel=f"SM $[{SM_UNIT}]$",
label="mean",
)
clim_std.plot(ax=axs[1], label="std.dev. $\sigma$", xlabel="Time [month]")
axs[1].legend()
if metric == "Absolute Anomalies":
var = "abs_anomaly"
ylabel = f"Anomaly $[{SM_UNIT}]$"
elif metric == "Relative Anomalies":
var = "rel_anomaly"
ylabel = f"Anomaly $[\%]$"
elif metric == "Z-Scores":
var = "z_score"
ylabel = f"Z-score $[\sigma]$"
else:
raise NotImplementedError(f"{metric} is not implemented")
axs[2].axhline(0, color="k")
axs[2].fill_between(
ts[var].index, ts[var].values, where=ts[var].values >= 0, color="blue"
)
axs[2].fill_between(
ts[var].index, ts[var].values, where=ts[var].values < 0, color="red"
)
axs[2].set_ylabel(ylabel)
axs[2].set_xlabel("Time [year]")
axs[2].set_title(f"Soil Moisture {metric} at Lon: {lon}° W, Lat: {lat}° N")
plt.tight_layout()
We now compute the the anomalies for the whole image stack (not only on a time series basis as in the previous example). For this we use some of the functions provided by xarray to group data. For the climatology (reference) we use all data from 1991 to 2020 (standard baseline period), but you can of course try a different period here as well by changing the years in the next cell. We then select all (absolute) soil moisture values in this period and group them by their month (i.e. all January, February, ... values for all years in the baseline period) and compute the mean for each group. This way we get a stack of 12 images (one for each month).
baseline = (1991, 2020)
baseline_slice = slice(f"{baseline[0]}-01-01", f"{baseline[1]}-12-31")
CLIM = (
DS.sel(time=baseline_slice)["sm"]
.groupby(DS.sel(time=baseline_slice).time.dt.month)
.mean()
)
display(CLIM)
<xarray.DataArray 'sm' (month: 12, lat: 152, lon: 204)>
dask.array<stack, shape=(12, 152, 204), dtype=float32, chunksize=(1, 152, 204), chunktype=numpy.ndarray>
Coordinates:
* lat (lat) float32 71.88 71.62 71.38 71.12 ... 34.88 34.62 34.38 34.12
* lon (lon) float32 -10.88 -10.62 -10.38 -10.12 ... 39.38 39.62 39.88
* month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
dtype: float32
units: m3 m-3
valid_range: [0. 1.]
long_name: Volumetric Soil Moisture
_CoordinateAxes: time lat lonWe can now use the climatology stack and compute the difference between each image of absolute soil moisture and the climatological mean of the same month. We assign the result to a new variable in the same stack called sm_anomaly.
%%capture --no-display
DS["sm_anomaly"] = DS["sm"] - CLIM.sel(month=DS.time.dt.month).drop("month")
We can use the bounding box chosen in the previous example to extract all soil moisture data in this area. We then compute the mean over all locations in the bounding box to get a single time series for the study area. Note that the coverage of C3S Soil Moisture varies over time (see the first example), which can also affect the value range of computed anomalies (standard deviation $\sigma$ is is usually larger for earlier periods).
subset = DS[["sm_anomaly"]].sel(
lon=slice(STUDY_AREA["bbox"][0], STUDY_AREA["bbox"][1]),
lat=slice(STUDY_AREA["bbox"][3], STUDY_AREA["bbox"][2]),
)
MEAN_TS = subset.mean(dim=["lat", "lon"]).to_pandas()
STD_TS = subset.std(dim=["lat", "lon"]).to_pandas()
Now we can not only visualize the monthly anomalies for all downloaded images, but also create a plot of annual mean values in the chosen study area. We see an overall trend towards drier conditions in most regions.
dates = [str(pd.to_datetime(t).date()) for t in DS["time"].values]
slider = widgets.SelectionSlider(
options=dates,
value=dates[-1],
description="Select a date to plot (map):",
continuous_update=False,
style={"description_width": "initial"},
layout=widgets.Layout(width="30%"),
)
@widgets.interact(date=slider)
def plot_anomaly(date: str):
STUDY_AREA_TS = pd.DataFrame(MEAN_TS["sm_anomaly"]).resample("A").mean()
STUDY_AREA_TS.index = STUDY_AREA_TS.index.year
fig = plt.figure(figsize=(15, 4), constrained_layout=True)
gs = fig.add_gridspec(1, 3)
map_ax = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())
ts_ax = fig.add_subplot(gs[0, 1:])
# Plot overview map:
p_anom = (
DS["sm_anomaly"]
.sel(time=date)
.plot(
transform=ccrs.PlateCarree(),
ax=map_ax,
cmap=plt.get_cmap("RdBu"),
cbar_kwargs={"label": f"Anomaly [{SM_UNIT}]"},
)
)
map_ax.axes.add_feature(cartopy.feature.LAND, zorder=0, facecolor="gray")
map_ax.axes.coastlines()
map_ax.add_feature(cartopy.feature.BORDERS)
map_ax.set_title(f"{date} - Soil Moisture Anomaly")
# Add study area box to map:
bbox = STUDY_AREA["bbox"]
map_ax.plot(
[bbox[0], bbox[0], bbox[1], bbox[1], bbox[0]],
[bbox[2], bbox[3], bbox[3], bbox[2], bbox[2]],
color="red",
linewidth=3,
transform=ccrs.PlateCarree(),
)
gl = map_ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, alpha=0.25)
gl.right_labe, gl.top_label = False, False
# Create the bar plot:
bars = STUDY_AREA_TS.plot(
kind="bar",
ax=ts_ax,
title=f"Annual conditions in `{STUDY_AREA['name']}` study area",
legend=False,
xlabel="Year",
ylabel=f"Anomaly [{SM_UNIT}]",
)
bars.axhline(y=0, color="black", linewidth=1)
# Highlight the corresponding bar(s) on the right:
for i, bar in enumerate(bars.patches):
year = STUDY_AREA_TS.index.values[i]
if STUDY_AREA_TS.values[i] > 0:
bar.set_facecolor("blue")
else:
bar.set_facecolor("red")
if year == pd.to_datetime(date).year:
bar.set_edgecolor("k")
bar.set_linewidth(3)